Summary of Mitigating Partial Observability in Sequential Decision Processes Via the Lambda Discrepancy, by Cameron Allen et al.
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy
by Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel metric called the λ-discrepancy for reinforcement learning algorithms in partially observable environments. The metric measures the difference between two temporal difference (TD) value estimates, each computed using TD(λ) with different values of λ. The authors show that the λ-discrepancy is zero for Markov decision processes and almost always non-zero for partially observable environments. They also demonstrate empirically that minimizing the λ-discrepancy can help learn a memory function to mitigate partial observability. To achieve this, they train a reinforcement learning agent that constructs two recurrent value networks with different λ parameters and minimizes the difference between them as an auxiliary loss. This approach scales well to challenging partially observable domains, outperforming a baseline recurrent agent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps AI agents learn better in situations where they don’t have complete information about their environment. It creates a new way for agents to figure out when they’ve found the right way to represent their world, and it does so without needing to know all the hidden details. This can lead to smarter decision-making in complex environments. |
Keywords
* Artificial intelligence * Reinforcement learning
